In the beginning, marketing automation platforms grew other channels and tools around their core of email marketing. The basic mode involved if/then rules: if a customer takes this action, show this response. But overlapping campaigns with if/then rules become very complicated very quickly, especially when you're talking about millions of customers, each one in a different frame of mind, and each expecting his/her own personalized experience. As a result, marketing platforms are evolving from their traditional if/then campaigns to the newer approach of customer journeys that are often guided by machine learning. It's the difference between setting up all the rules for the encounter on the one hand, B2B marketing startup YesPath CEO Jason Garoutte told me, and employing something like Netflix's recommendation engine, on the other. "Netflix doesn't write rules about what [movie] you should watch next," he pointed out.

Newspapers and politicians around the world have been reacting to Boris Johnson's appointment as UK foreign secretary. Many were surprised, citing his history of faux pas including insulting the president of Turkey and commenting on the US president's ancestry. Here we take a look at the response in countries where Mr Johnson will now represent the UK. The Washington Post publishes a round-up of "undiplomatic" things Mr Johnson has said during his time in public life. Washington Post writer Ishaan Tharoor also writes that Mr Johnson "has controversially bucked the Western trend and praised Syrian President Bashar al-Assad for battling the Islamic State, no matter its parallel campaign of violence on Syria's civilian population".

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

AI and machine learning changes the software paradigm computers have been based on for many decades. In the traditional computing domain, providing an input, we feed it into an algorithm to produce the desired output. This is the rule-based framework the majority of the systems around us still work with. We set up our thermostat to a desire temperature (input) and a rule based programming (algorithm) will take care of reading a sensor and activating heating or AC machines to get to the room temperature we want (output). The industry has been working relentlessly for many years developing better hardware, software and apps to solve a gazillion problems and use cases around us with programmable solutions.

Danaher/Institute for Ethics and Emerging TechnologiesAlgorithms are everywhere, and in most ways they make our lives better. In the simplest terms, algorithms are procedures or formulas aimed at solving problems. Implemented on computers, they sift through big databases to reveal compatible lovers, products that please, faster commutes, news of interest, stocks to buy, and answers to queries. Dud dates or boring book recommendations are no big deal. But John Danaher, a lecturer in the law school at the National University of Ireland, warns that algorithmic decision-making takes on a very different character when it guides government monitoring and enforcement efforts.

Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Another issue is minimality of arguments.

Plenty of media attention has been devoted to robots replacing lawyers. Conversely, some industry players claim that artificial intelligence (AI) is simply a buzzword used to sell software to law firms. "Are many AI-badged products just rule-based decision-making tools?" asks Alex Smith, platform innovation lead at LexisNexis UK. "What counts as AI?" AI in business has moved beyond process automation to include natural language processing and machine-learning, whereby computers are trained to interpret information and adjust their processes to user feedback. Rather than searching for keywords or strings of words, the software reads and understands information, so its findings and recommendations are based on contextual elements. Gerard Frith, chief executive of AI consultancy Matter, explains how AI adds value by modelling and reapplying expert knowledge in a fast, scalable way.

NFL coaching great Buddy Ryan, who passed away Tuesday at the age of 82, didn't have difficulty finding the spotlight wherever he went. From the New York Jets when they became the first AFL team to win a Super Bowl in 1968, to the Super Bears of 1985 to his last stop as the head man of the Arizona Cardinals in 1994-95, Ryan had a common theme -- "You got a winner in town." One of the best fits for that boisterous personality was his stop as head coach of the Philadelphia Eagles from 1986 to 1990. He was brash, but his teams backed up his words. He made fun of the rich owner, Norman Braman -- "that guy in France" -- who spent a lot of time overseas, and connected with the blue-collar town.

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization's business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans.